Deep generative modelling for nonlinear analysis and in-situ assessment of masonry using multiple mechanical fields

IF 7.4 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Ahmad Adaileh , Bahman Ghiassi , Riccardo Briganti
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引用次数: 0

Abstract

Design and in-situ assessment of masonry structures is a challenging task due to the brittle and nonlinear nature of this widely used material, the complex interaction between its components, and the vast variability of material properties in its design space. Current methodologies often rely on oversimplified assumptions that inadequately capture the true mechanical behaviour of masonry or require extensive knowledge and expertise for reliable implementation or interpretation of the obtained results. To overcome these limitations, this article presents an innovative generative machine learning approach, based on conditional generative adversarial network (cGAN), that allows establishing a direct or reverse link between masonry meso-structure and multiple mechanical fields without any specific knowledge of the properties or constitutive laws. The developed cGAN model interprets relationships among multiple mechanical maps using a single model, which leads to enhanced predictions in both linear and nonlinear stages for a wide range of unseen scenarios. The model shows an excellent capability to capture the effect of local and global variability of material properties, constituents sizes, and loading scenarios on the results in both direct (i.e. predicting the strain maps from meso-structure and material properties) and reverse (i.e. predicting the meso-structure and material properties from strain maps) problems. The proposed cGAN modelling approach emerges as a versatile tool with potential broad applications for the design and evaluation of nonlinear composite materials and mechanical behaviour of materials in general, addressing a wide spectrum of engineering challenges.
基于多力学场的砌体非线性分析与原位评估的深度生成模型
砌体结构的设计和现场评估是一项具有挑战性的任务,因为这种广泛使用的材料具有脆性和非线性的性质,其组成部分之间复杂的相互作用,以及在其设计空间中材料性能的巨大可变性。目前的方法通常依赖于过于简化的假设,这些假设不能充分捕捉砖石的真实力学行为,或者需要广泛的知识和专业知识才能可靠地实施或解释所获得的结果。为了克服这些限制,本文提出了一种基于条件生成对抗网络(cGAN)的创新生成机器学习方法,该方法允许在砖石细看结构和多个力学领域之间建立直接或反向联系,而无需任何特性或本构律的特定知识。开发的cGAN模型使用单个模型解释多个机械图之间的关系,从而增强了对各种未知场景的线性和非线性阶段的预测。该模型在直接(即从细观结构和材料属性预测应变图)和反向(即从应变图预测细观结构和材料属性)问题上显示出出色的能力,可以捕捉材料属性、组分尺寸和加载场景的局部和全局可变性对结果的影响。提出的cGAN建模方法作为一种通用工具,在非线性复合材料的设计和评估以及材料的力学行为方面具有潜在的广泛应用,解决了广泛的工程挑战。
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来源期刊
Construction and Building Materials
Construction and Building Materials 工程技术-材料科学:综合
CiteScore
13.80
自引率
21.60%
发文量
3632
审稿时长
82 days
期刊介绍: Construction and Building Materials offers an international platform for sharing innovative and original research and development in the realm of construction and building materials, along with their practical applications in new projects and repair practices. The journal publishes a diverse array of pioneering research and application papers, detailing laboratory investigations and, to a limited extent, numerical analyses or reports on full-scale projects. Multi-part papers are discouraged. Additionally, Construction and Building Materials features comprehensive case studies and insightful review articles that contribute to new insights in the field. Our focus is on papers related to construction materials, excluding those on structural engineering, geotechnics, and unbound highway layers. Covered materials and technologies encompass cement, concrete reinforcement, bricks and mortars, additives, corrosion technology, ceramics, timber, steel, polymers, glass fibers, recycled materials, bamboo, rammed earth, non-conventional building materials, bituminous materials, and applications in railway materials.
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